Papers with Code 아카이브
Entity Resolution
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Entity resolution (also known as entity matching, record linkage, or duplicate detection) is the task of finding records that refer to the same real-world entity across different data sources (e.g., data files, books, websites, and databases). (Source: Wikipedia)
Surveys on entity resolution:
Christophides et al.: End-to-End Entity Resolution for Big Data: A Survey, 2020.
Barlaug and Gulla: Neural Networks for Entity Matching: A Survey, 2021.
The task of entity resolution is closely related to the task of entity alignment which focuses on matching entities between knowledge bases. The task of entity linking differs from entity resolution as entity linking focuses on identifying entity mentions in free text.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ltkong218/mdflow
공식 코드MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation
https://github.com/martanit/LEAD
공식 코드Effective Model of Loop Extrusion Predicts Chromosomal Domains
https://github.com/megagonlabs/ditto
공식 코드https://github.com/metaagentx/opencaptchaworld
공식 코드https://github.com/mhmoslemi2338/pre-EM-bias
공식 코드https://github.com/micdoh/XLRON
공식 코드Reinforcement Learning for Dynamic Resource Allocation in Optical Networks: Hype or Hope?
https://github.com/microsoft/StyleSwin
공식 코드StyleSwin: Transformer-based GAN for High-resolution Image Generation
https://github.com/microsoft/dcvc
공식 코드https://github.com/mitao-cat/feature-level_bias
공식 코드Understanding and Counteracting Feature-Level Bias in Click-Through Rate Prediction
https://github.com/mullervasconcelos/ieee-tac2023
공식 코드Robust one-shot estimation over shared networks in the presence of denial-of-service attacks
https://github.com/naserahmadi/tdmatch
공식 코드https://github.com/newiz430/bandana
공식 코드https://github.com/ngmarchant/dblink
공식 코드https://github.com/nju-websoft/Remp
공식 코드Crowdsourced Collective Entity Resolution with Relational Match Propagation
https://github.com/noise-lab/automated-dns-censorship
공식 코드Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning
https://github.com/olivierbinette/er-evaluation
공식 코드https://github.com/openai/multiagent-competition
공식 코드https://github.com/openmicg/mcg
공식 코드https://github.com/paragdakle/emailcoref
공식 코드CEREC: A Corpus for Entity Resolution in Email Conversations
https://github.com/patentsview/patentsview-evaluation
공식 코드Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.org
https://github.com/philine-bommer/dl4s2s
공식 코드Deep Learning Meets Teleconnections: Improving S2S Predictions for European Winter Weather
https://github.com/qlm-project/qlm
공식 코드Queue management for slo-oriented large language model serving
https://github.com/quentin-duchemin/goodness-of-fit-MC
공식 코드Concentration inequality for U-statistics of order two for uniformly ergodic Markov chains
https://github.com/raoyongming/DynamicViT
공식 코드DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
https://github.com/rascalgdd/pmnet
공식 코드https://github.com/ravice234/cryptoasset-attribution-tag-linker
공식 코드Linking Cryptoasset Attribution Tags to Knowledge Graph Entities: An LLM-based Approach
https://github.com/rebeccaeexu/cit-ec
공식 코드https://github.com/s-mohammad-hashemi/sst
공식 코드https://github.com/siwangzhou/deepbcs
공식 코드Multi-Channel Deep Networks for Block-Based Image Compressive Sensing
https://github.com/skyhehe123/scatterformer
공식 코드ScatterFormer: Efficient Voxel Transformer with Scattered Linear Attention
https://github.com/sntcristian/and-kge
공식 코드A Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literals
https://github.com/spring-epfl/sinbad
공식 코드SINBAD: Saliency-informed detection of breakage caused by ad blocking
https://github.com/sunblaze-ucb/progent
공식 코드https://github.com/superhero-7/altdiffuson
공식 코드https://github.com/tdelgado00/learning-synthesis
공식 코드Exploration Policies for On-the-Fly Controller Synthesis: A Reinforcement Learning Approach
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.